Number Of Active Sensor Nodes Research Articles

An Improved Genetic Algorithm for Scheduling Sensor Nodes in WSN

The extended lifetime of Wireless Sensor Networks (WSN) is an attractive goal for various types of research. This can be achieved if not all sensor nodes in the network consume their energy; this is because the energy consumption of every sensor node is a vital resource for a WSN. The scheduling techniques have recently enticed the interest of the researchers’ community, as they provide the ability to adjust a set of nodes in sleep mode instead of activating all sensor nodes. However, we consider the sensors that were selected to be in sleep mode, which will not affect network coverage for any target or full connectivity. In this paper, the genetic algorithm has been used to build efficient scheduling for the sensor nodes, which were based on multiple objectives in the fitness function, and we have proposed improved mutation and crossover operations. Then, we evaluate our approaches in a target tracking application compared with previous GA approaches. Our simulations show that we have an optimal chromosome that contains a minimum number of active sensor nodes for scheduling within 3-5 iterations.

An Energy-Efficient Routing Algorithm for WSNs Using Fuzzy Logic.

Battery replacement or recharging is essential for sensor nodes because they are typically powered by batteries in wireless sensor network (WSN) applications. Therefore, creating an energy-efficient data transfer technique is required. The base station (BS) receives data from one sensor node and routes the data to another sensor node. As a result, an energy-efficient routing algorithm using fuzzy logic (EERF) represents a novel approach that is suggested in this study. One of the reasoning techniques utilized in scenarios where there is a lot of ambiguity is fuzzy logic. The remaining energy, the distance between the sensor node and the base station, and the total number of connected sensor nodes are all inputs given to the fuzzy system of the proposed EERF algorithm. The proposed EERF is contrasted with the current systems, like the energy-aware unequal clustering using fuzzy logic (EAUCF) and distributed unequal clustering using fuzzy logic (DUCF) algorithms, in terms of evaluation criteria, including energy consumption, the number of active sensor nodes for each round in the network, and network stability. EAUCF and DUCF were outperformed by EERF.

SALA-IoT: Self-Reduced Internet of Things With Learning Automaton Sleep Scheduling Algorithm

The extensive development of wireless communications has led to the popularity of the Self-Powered Internet of Things (SPIoT) networks. Even though significant advances made in keeping energy consumption at a low level, making such networks live longer are still one of the biggest challenges. In particular, a significant question is how to cover the maximum range of the environment by the sensor nodes with the lowest amount of energy. In this research, we have proposed a sleep Scheduling Algorithm based on Learning Automaton for IoT (SALA-IoT), utilizing machine learning approaches to find the optimal set of sensor nodes that can cover a wide range of the environment. This algorithm consists of learning and covering phases, in which the essential sensor nodes are activated, and the rest are turned off. Although the implementation of learning algorithms requires a high energy level, after the learning phase’s performance, the network gathers complete information about the status of the nodes. It can meet the needs of the network with a limited amount of energy. We have evaluated the proposed algorithm with several simulation scenarios and considered the coverage area and the number of active sensor nodes as the main evaluation metrics. The simulation results show that the sensors sensing and covering ranges are directly related to their transmitted power, and the transmitted power of the sensors can be adjusted to match the expected network requirements in terms of primary coverage factors; the proposed method improved by 6.743 for 50 nodes and 5.204 for 100 nodes, respectively, for distance and intervals in a different number of nodes.

An energy-aware and Q-learning-based area coverage for oil pipeline monitoring systems using sensors and Internet of Things

Pipelines are the safest tools for transporting oil and gas. However, the environmental effects and sabotage of hostile people cause corrosion and decay of pipelines, which bring financial and environmental damages. Today, new technologies such as the Internet of Things (IoT) and wireless sensor networks (WSNs) can provide solutions to monitor and timely detect corrosion of oil pipelines. Coverage is a fundamental challenge in pipeline monitoring systems to timely detect and resolve oil leakage and pipeline corrosion. To ensure appropriate coverage on pipeline monitoring systems, one solution is to design a scheduling mechanism for nodes to reduce energy consumption. In this paper, we propose a reinforcement learning-based area coverage technique called CoWSN to intelligently monitor oil and gas pipelines. In CoWSN, the sensing range of each sensor node is converted to a digital matrix to estimate the overlap of this node with other neighboring nodes. Then, a Q-learning-based scheduling mechanism is designed to determine the activity time of sensor nodes based on their overlapping, energy, and distance to the base station. Finally, CoWSN can predict the death time of sensor nodes and replace them at the right time. This work does not allow to be disrupted the data transmission process between sensor nodes and BS. CoWSN is simulated using NS2. Then, our scheme is compared with three area coverage schemes, including the scheme of Rahmani et al., CCM-RL, and CCA according to several parameters, including the average number of active sensor nodes, coverage rate, energy consumption, and network lifetime. The simulation results show that CoWSN has a better performance than other methods.

An Area Coverage Scheme Based on Fuzzy Logic and Shuffled Frog-Leaping Algorithm (SFLA) in Heterogeneous Wireless Sensor Networks

Coverage is a fundamental issue in wireless sensor networks (WSNs). It plays a important role in network efficiency and performance. When sensor nodes are randomly scattered in the network environment, an ON/OFF scheduling mechanism can be designed for these nodes to ensure network coverage and increase the network lifetime. In this paper, we propose an appropriate and optimal area coverage method. The proposed area coverage scheme includes four phases: (1) Calculating the overlap between the sensing ranges of sensor nodes in the network. In this phase, we present a novel, distributed, and efficient method based on the digital matrix so that each sensor node can estimate the overlap between its sensing range and other neighboring nodes. (2) Designing a fuzzy scheduling mechanism. In this phase, an ON/OFF scheduling mechanism is designed using fuzzy logic. In this fuzzy system, if a sensor node has a high energy level, a low distance to the base station, and a low overlap between its sensing range and other neighboring nodes, then this node will be in the ON state for more time. (3) Predicting the node replacement time. In this phase, we seek to provide a suitable method to estimate the death time of sensor nodes and prevent possible holes in the network, and thus the data transmission process is not disturbed. (4) Reconstructing and covering the holes created in the network. In this phase, the goal is to find the best replacement strategy of mobile nodes to maximize the coverage rate and minimize the number of mobile sensor nodes used for covering the hole. For this purpose, we apply the shuffled frog-leaping algorithm (SFLA) and propose an appropriate multi-objective fitness function. To evaluate the performance of the proposed scheme, we simulate it using NS2 simulator and compare our scheme with three methods, including CCM-RL, CCA, and PCLA. The simulation results show that our proposed scheme outperformed the other methods in terms of the average number of active sensor nodes, coverage rate, energy consumption, and network lifetime.

Energy-efficient coverage optimization in wireless sensor networks based on Voronoi-Glowworm Swarm Optimization-K-means algorithm

Deployment of sensor nodes is one of the crucial factors in mobile wireless sensor networks for improving the performance of the network. The network's lifetime primarily depends on the consumed energy and area coverage by the sensor nodes. The efficiency of mobile wireless sensor networks increases by the efficient deployment of the sensors. Coverage and energy consumption mainly depends on the effective deployment schemes of sensors. This article presents an energy-efficient coverage optimization technique with the help of the Voronoi-Glowworm Swarm Optimization-K-means algorithm. In this approach, Glowworm Swarm Optimization, K-means algorithm, and Voronoi cell structure enhance the coverage area with a minimum number of active nodes. This approach considers optimum sensing radius calculation for efficient sensor deployment. Furthermore, the proposed method improves the lifetime of the deployed network by decreasing the consumed energy by the deployed sensor nodes using multi-hop transmission and the sleep-wake mechanism. The simulation result shows that area coverage is achieved by the proposed method up to 99.99% with the optimum number of active sensor nodes.

Energy-Efficient Load Balancing Strategy for Wireless Sensor Networks using Quasi-oppositional based Jaya Optimization

The area of wireless sensor networks (WSNs) has gained significant attention from researchers due to its expansive range of applications, such as industrial regulation, human detection, and medical diagnosis. WSN is a vast number of geographically scattered devices that use sensor nodes to communicate and gather information from the target region. In clustered WSNs, the cluster head (CH) is deliberated as the relay node at a higher energy level than the non-CH nodes. The relay nodes perform more tasks related to the non-CHs, and these relay nodes are restricted by energy and communication capacity. Therefore, balancing the load of the relay node is a significant concern for improving the performance of the WSNs. Clustering is a well-known technique enforced to balance the load of the relay nodes. Often, densely loaded relay nodes dissolve their energy in less time and may cause changes in the topology of the network. In this study, we propose a Quasi-oppositional based Jaya load balancing strategy (QOJ-LBS) with a novel fitness function to address the issue of load balancing. The novel fitness function derived from the convex combination of the least lifespan of the relay node across the network and the entropy value of the lifespan of all relay nodes. The proposed QOJ-LBS justifies the network performance under two different WSN conditions called scenario-1 and scenario-2 with single-hop and multi-hop routing. The experimental analysis shows that improvement in network lifespan, total energy utilization, and the number of active sensor nodes of WSN is statistically significant in proposed QOJ-LBS compared to other state-of-the-art algorithms.

A Modified GA-Based Load Balanced Clustering Algorithm for WSN

The prevalent applications of WSN have fascinated a plethora of research efforts. Sensor nodes have serious limitations such as battery lifetime, memory constraints, and computational capabilities. Clustering is an important method for maximizing the network lifetime. In clustering, a network is divided into virtual groups, and CHs send their data to the BS either directly or using multi-hop routing. CHs are some special nodes having more energy than normal nodes. In fact, these special nodes are also battery operated and consequently power constrained; thus, they play a vital role in network lifetime. Cluster formation is very important and improper design may cause overload. This paper presents a modified GA-based load balanced clustering (MGALBC) algorithm for WSN. It is better than GA-based load balanced clustering (GALBC) algorithm because it balances the load by considering the residual energy. The result shows that the proposed method is better than GALBC in terms of energy consumption, number of active sensor nodes and network life.

Determination of the Multicast Optimal Route for Mobile Sinks in a Specified Deadline Using Network Coding and Tabu Search Algorithm in Wireless Sensor Networks

The purpose of this paper is to determine a multicast optimal route (OR) at a specific deadline for mobile sinks (MSs) using network coding (NC) in wireless sensor networks (WSNs). For this purpose, we first show that the solution of the problem of OR of MSs (ORM) is NP-hard; then to solve this problem, we propose several convex optimization models based, on the mixed integer linear model (MILP). In these models, NC and support vector regression (SVR) methods are used, and the difference of the models is based on their objective function, which includes maximizing the fit value, minimizing the total energy consumption of the active sensor nodes (ASNs), minimizing the energy consumption of the entire network, and maximizingthe remaining energy of ASNs. These models cannot be solved in polynomial time because they have several parameters and WSNs resources are limited. For this purpose, we propose an algorithm based on the tabu search (TS) algorithm. In the simulation section, we compare the proposed algorithm and optimization models with the famous traveling salesman problem (TSP). The results show the superiority of the proposed models and algorithm based on the deadline, the number of ASNs in energy consumption and network lifetime and calculation time. Each model based on its objective function improves energy consumption by 23% and network lifetime by 16%. The reason for this excellence is the use of NC in models. However, the calculation time in the models is 8% more than the proposed and TSP algorithms.

A distributed reinforcement learning based sensor node scheduling algorithm for coverage and connectivity maintenance in wireless sensor network

The fundamental challenge for randomly deployed resource-constrained wireless sensor network is to enhance the network lifetime without compromising its performance metrics such as coverage rate and network connectivity. One way is to schedule the activities of sensor nodes and form scheduling rounds autonomously in such a way that each spatial point is covered by at least one sensor node and there must be at least one communication path from the sensor nodes to base station. This autonomous activity scheduling of the sensor nodes can be efficiently done with Reinforcement Learning (RL), a technique of machine learning because it does not require prior environment modeling. In this paper, a Nash Q-Learning based node scheduling algorithm for coverage and connectivity maintenance (CCM-RL) is proposed where each node autonomously learns its optimal action (active/hibernate/sleep/customize the sensing range) to maximize the coverage rate and maintain network connectivity. The learning algorithm resides inside each sensor node. The main objective of this algorithm is to enable the sensor nodes to learn their optimal action so that the total number of activated nodes in each scheduling round becomes minimum and preserves the criteria of coverage rate and network connectivity. The comparison of CCM-RL protocol with other protocols proves its accuracy and reliability. The simulative comparison shows that CCM-RL performs better in terms of an average number of active sensor nodes in one scheduling round, coverage rate, and energy consumption.

Load Balanced Clustering Based on Imperialist Competitive Algorithm in Wireless Sensor Networks

Energy consumption is one of the most serious issues in designing Wireless Sensor Networks (WSNs) for maximizing its lifetime and stability. Clustering is considered as one of the topology control methods for maintaining the stability of WSNs which can significantly reduce energy consumption in WSNs. However, using different methods for the selection of cluster head is an important challenge in this domain of research. Load balanced clustering is known as an NP-hard problem for a WSN along with unequal load for sensor nodes. The Imperialist Competitive Algorithm (ICA) is regarded as an evolutionary method which can be used for finding a quick and efficient solution to such problems. In this paper, a clustering method with an evolutionary approach is introduced which investigates the issues of load balance and energy consumption of WSNs in the equal and unequal load modes so as to select optimal cluster heads. Simulation of the proposed method, carried out via NS2, indicated that it improves the criteria of energy consumption, the number of active sensor nodes and execution time.

Quasi-dynamic Load Balanced Clustering Protocol for Energy Efficient Wireless Sensor Networks

Miscellaneous issues related to wireless sensor networks (WSN) like clustering of sensor nodes, load balancing amongst the cluster heads, energy efficient multipath routing, prevention of communication failures, storage of information etc. are receiving increasing interest now a days. Hence, addressing these issues with the restriction of computational constraint and limited power supply is a challenging task to be performed. In regard to this, design of energy efficient clustering and routing algorithms have gained proper attention over the last few years because of their contribution towards the appropriate utilization of available energy and extension of network lifetime. In this paper, we propose a quasi dynamic clustering algorithm with an objective to prolong the network lifetime by reducing energy consumption of sensor nodes. Entire set of sensor nodes has been divided into two sets depending upon their proximity to the nearest gateways. Nodes from the first set are assigned to the fixed set of gateways while those from the other set are associated dynamically on the basis of instantaneous network statistics. Performance of the proposed algorithm has been demonstrated by means of extensive simulation studies. It has finally been substantiated that the proposed clustering protocol shows comprehensive improvement with respect to existing algorithms in terms of number of active sensor nodes, network life time, energy consumption etc.

A Software-Defined Surveillance System With Energy Harvesting: Design and Performance Optimization

Even though energy harvesting is a promising technology for energy-efficient surveillance systems, energy harvesting levels are highly dynamic depending on the time and location. Thus, the deployment of nonenergy-harvesting sensor nodes (NHSs) and sophisticated sleep scheduling of sensor nodes are necessary for performance guaranteed surveillance systems. In this paper, we present a software-defined surveillance system (SDSS) in which a centralized controller determines the sleep schedules of energy harvesting and NHSs on the basis of the collected information such as the spatial distribution of targets and the energy levels of sensor nodes. To derive the optimal sleep schedules minimizing the number of active sensor nodes while providing sufficient surveillance performance, a constraint Markov decision process problem is formulated and the optimal policy on sleep scheduling is obtained by linear programming. The evaluation results demonstrate that the SDSS with the optimal policy can reduce energy consumption by employing fewer active sensor nodes while providing the required level of target monitoring probability.

Optimization of Wireless Sensor Networks using PPSS Algorithm

In the development of various large-scale sensor systems, a particularly challenging problem is how to dynamically organize the sensors into a wireless communication network and route sensed information from the field sensors to a target system. The prime motivation of our work is to balance the inherent trade-off between the resource consumption and the accuracy of the target tracking in wireless sensor networks. Toward this objective, the study goes through a new energy-efficient dynamic optimization-based sleep scheduling and target prediction technique for large-scale sensor networks. We present a probability-based prediction and optimization-based sleep scheduling protocol (PPSS) to improve energy efficiency of proactive wake up. A cluster-based scheme is exploited for optimization-based sleep scheduling. At every sampling instant, only one cluster of sensors that located in the proximity of the target is activated, whereas the other sensors are inactive. To activate the most appropriate cluster, we propose a non myopic rule, which is based on not only the target state prediction but also its future tendency. Finally, the effectiveness of the proposed approach is evaluated and compared with the state-of-the-art protocols in terms of tracking accuracy, inter node communication, and computation complexity . Index Terms—Energy efficiency, target prediction, sleep scheduling, target tracking, sensor networks. In a large-scale sensor network, hundreds or thousands of tiny sensor nodes are randomly deployed into a monitoring field to gather data. The complexity of computation and communication increases with the number of active sensor nodes tracking the target. The amount of energy used in the network is proportional to the number of active sensor nodes. It is best for sensor nodes to be arranged into collaborative m groups. Group collaboration should be limited to a tracking area around the target so that the communication and computation will be independent of the size of the network. Multiple nodes surrounding the target may collaborate and gather information. The tracking accuracy and performance is limited to the information in those sensors. In a large-scale sensor network, it is important to locate the target with high accuracy while consuming the least amount of energy. Some of the existing studies have focused on energy efficient methods to track mobile targets. Our objective is to propose a simple routing metric that is composed of the energy expenditure and battery power of a node. Therefore, the cluster activation phase has a great importance not only in minimizing energy consumption but also improve the optimized tracking accuracy.

A Power-Efficient Clustering Protocol for Coal Mine Face Monitoring with Wireless Sensor Networks Under Channel Fading Conditions.

This study proposes a novel power-efficient and anti-fading clustering based on a cross-layer that is specific to the time-varying fading characteristics of channels in the monitoring of coal mine faces with wireless sensor networks. The number of active sensor nodes and a sliding window are set up such that the optimal number of cluster heads (CHs) is selected in each round. Based on a stable expected number of CHs, we explore the channel efficiency between nodes and the base station by using a probe frame and the joint surplus energy in assessing the CH selection. Moreover, the sending power of a node in different periods is regulated by the signal fade margin method. The simulation results demonstrate that compared with several common algorithms, the power-efficient and fading-aware clustering with a cross-layer (PEAFC-CL) protocol features a stable network topology and adaptability under signal time-varying fading, which effectively prolongs the lifetime of the network and reduces network packet loss, thus making it more applicable to the complex and variable environment characteristic of a coal mine face.

Multivariated Bayesian Compressive Sensing in Wireless Sensor Networks

Recently, compressive sensing has been studied in wireless sensor networks, which allows an aggregator to recover the desired sparse signal with fewer active sensor nodes. In this paper, we consider heterogeneous sensing environments, where the sensing quality varies due to the differences in the physical environment of each sensor node. We consider a Bayesian compressive sensing approach and propose two efficient algorithms that decrease the number of active sensor nodes while maintaining high performance. Both the selection algorithms aim to reduce the estimation error by minimizing the determinant of the error covariance matrix, which is proportional to the volume of the confidence ellipsoid. The first algorithm is the centralized greedy selection algorithm, which can achieve a nearly optimal solution in terms of the minimum confidence ellipsoid. It can also achieve almost the same level of performance as the combinatorial selection method, but has a lower complexity and outperforms the conventional convex relaxation method. The second algorithm is the decentralized selection algorithm, which is derived by approximating the determinant of the error covariance matrix. Unlike the centralized greedy algorithm, it can be done by each sensor node without heavy overhead or high complexity. Furthermore, we prove that the decentralized selection algorithm becomes equivalent to the centralized greedy algorithm as the number of sensor nodes increases. Our simulation results show that the centralized greedy selection algorithm provides the best performance while the decentralized algorithm performs nearly as well as the centralized algorithm as the number of sensor nodes increases.

On Connected Target k-Coverage in Heterogeneous Wireless Sensor Networks.

Coverage and connectivity are two important performance evaluation indices for wireless sensor networks (WSNs). In this paper, we focus on the connected target k-coverage (CTC) problem in heterogeneous wireless sensor networks (HWSNs). A centralized connected target k-coverage algorithm (CCTC) and a distributed connected target k-coverage algorithm (DCTC) are proposed so as to generate connected cover sets for energy-efficient connectivity and coverage maintenance. To be specific, our proposed algorithms aim at achieving minimum connected target k-coverage, where each target in the monitored region is covered by at least k active sensor nodes. In addition, these two algorithms strive to minimize the total number of active sensor nodes and guarantee that each sensor node is connected to a sink, such that the sensed data can be forwarded to the sink. Our theoretical analysis and simulation results show that our proposed algorithms outperform a state-of-art connected k-coverage protocol for HWSNs.

AMOF: adaptive multi-objective optimization framework for coverage and topology control in heterogeneous wireless sensor networks

In this paper, we propose adaptive multi-objective optimization framework based on non-dominated sorting genetic algorithm-II and learning automata (LA) for coverage and topology control in heterogeneous wireless sensor networks. The multi-objective optimization approach of the proposed framework, called MOOCTC (multi-objective optimization coverage and topology control), can simultaneously optimize several conflicting issues such as number of active sensor nodes, coverage rate of the monitoring area and balanced energy consumption while maintaining the network connectivity. This approach incorporates problem-specific knowledge in its operators to find high-quality solutions. In addition, this approach uses LA to dynamically adapt the crossover and mutation rates without any external control to improve the behavior of the optimization algorithm. Simulation results demonstrate the efficiency of the proposed multi-objective optimization approach in terms of lifetime, coverage and connectivity.

A study of three-dimensional optical code-division multiple-access for optical fiber sensor networks

This study proposes a novel optical fiber sensor (OFS) network using three-dimensional (3-D) wavelength/time/spatial optical code-division multiple-access (OCDMA) scheme. The proposed 3-D modified quadratic congruence/M-matrix (MQC/M-matrix) coding scheme overcome the restriction of single pulse per row inherent in traditional three-dimensional codes and maintain a high signal-to-noise ratio even when source power is low. The 3-D system is implemented using optical switches (OSW), fiber-Bragg gratings (FBGs), optical splitter and optical combiner. The noises of phase-induced intensity noise (PIIN) and multiple access interference (MAI) in the decoding mechanism can be suppressed by using a spectral spreading scheme and balance-detection in the receivers. By constructing 3-D codes using bipolar pseudorandom (PN) codes rather than unipolar codes provides a significant increase in the maximum permissible number of active sensor nodes.

RPLL - Rendezvous protocol for long-living sensor node

Sensor nodes (SNs), as constituents of wireless sensor network (WSN), are battery-powered not rechargeable devices and have limited amount of energy available. Since the lifetime of SNs is a crucial parameter for energy-efficient WSN design, it is essential to extend their lifetimes as much as possible. Here we propose a rendezvous scheme called Rendezvous Protocol for Long-Living SN, RPLL. This scheme is based on implementation of a duty-cycling technique. For each SN within WSN a unique identification number (ID) is allocated, thanks to which a collision problem is effectively remedied. The RPLL provides an on time wake-up of SNs in a fully decentralized way and fast detection of new appended SNs. Taking into account the WSN and SN working parameters, such as beacon time, beacon period, number of active SNs, and quartz oscillator instability, by using the proposed method, a WSN designer can determine the maximal lifetime of a SN, i.e. achieve optimal energy consumption.